Adaptive Distance Detection and Adaptive Instance Segmentation

원문 초록

This paper is about object detection model and semantic segmentation model in an FPS game Counter Strike: Condition Zero to detect enemies. Semantic segmentation is pixel-wise detection algorithm. We customized the architecture for semantic segmentation. We used YOLOv5s to train object detection model. After training, the inference time of semantic segmentation was compared to object detection. It was observed that object detection was slightly faster and semantic segmentation is slower but better at detecting the exact shape of the object. However, we found out that this slight difference of inference can grow exponentially if the both models get more complex. To solve this, we propose a novel approach called Adaptive distance detection which combines both algorithms. We will also introduce Adaptive instance segmentation to combine semantic segmentation and panoptic segmentation.